code: def _validate_not_gpu_instance_type(training_instance_type):
      """Placeholder docstring."""
      gpu_instance_types = [
          "ml.p2.xlarge",
          "ml.p2.8xlarge",
          "ml.p2.16xlarge",
          "ml.p3.xlarge",
          "ml.p3.8xlarge",
          "ml.p3.16xlarge",
      ]
  
      if is_pipeline_variable(training_instance_type):
          warn_msg = (
              "instance_type is a PipelineVariable (%s). "
              "Its interpreted value in execution time should not be of GPU types "
              "since GPU training is not supported for Scikit-Learn."
          )
          logger.warning(warn_msg, type(training_instance_type))
          return
  
      if training_instance_type in gpu_instance_types:
          raise ValueError(
              "GPU training is not supported for Scikit-Learn. "
              "Please pick a different instance type from here: "
              "https://aws.amazon.com/ec2/instance-types/"
          )
  
file_path: sagemaker-python-sdk/src/sagemaker/sklearn/estimator.py
imports: from __future__ import absolute_import
  import logging
  from typing import Union, Optional, Dict
  from sagemaker import image_uris
  from sagemaker.deprecations import renamed_kwargs
  from sagemaker.estimator import Framework
  from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, validate_version_or_image_args
  from sagemaker.sklearn import defaults
  from sagemaker.sklearn.model import SKLearnModel
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow import is_pipeline_variable
label_desc: Use image node_id 1 for calling a global function with example usage: _validate_not_gpu_instance_type(training_instance_type) without return types